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OSIRRC 2019 Jig

This is the jig for the SIGIR 2019 Open-Source IR Replicability Challenge (OSIRRC 2019). Check out the OSIRRC 2019 image library for a list of images that have been contributed to this exercise.

What's a jig?

To get started, clone the jig, and then download + compile trec_eval (inside the jig directory) with the following command:

git clone https://github.com/usnistgov/trec_eval.git && make -C trec_eval

Make sure the Docker Python package is installed (via pip, conda, etc.):

pip install -r requirements.txt

Make sure the Docker daemon is running.

For common test collections, topics and qrels are already checked into this repo.

To test the jig with an Anserini image using default parameters, try:

python run.py prepare \
    --repo osirrc2019/anserini \
    --collections [name]=[path]=[format] ...

then

python run.py search \
    --repo osirrc2019/anserini \
    --collection [name] \
    --topic /path/to/topic \
    --output /path/to/output \
    --qrels /path/to/qrels

Change:

• [name] and [path] to the collection name and path on the host, respectively
• [format] is one of trectext, trecweb, json, or warc
• /path/to/topic to the path of the topic file
• /path/to/output to the desired output directory.
• /path/to/qrels to the path of appropriate qrels file

The output run files will appear in the argument of --output. The full command line parameters are below.

To run a container (from a saved image) that you can interact with, try:

python run.py interact \
    --repo osirrc2019/anserini \
    --tag latest

Collections

The following collections are supported:

Command Line Options

Options with none as the default are required.

Command Line Options - prepare

python run.py prepare <options>

Command Line Options - search

python run.py search <options>

Command Line Options - train

python run.py train <options>

Command Line Options - interact

Docker Container Contract

Currently we support four hooks: init, index, search,and interact. We expect search or interact to be called after init and index. We also expect these four executables to be located in the root directory of the container.

Each script is executed with the interpreter determined by the shebang so you can use #!/usr/bin/env bash, #!/usr/bin/env python3, etc - just remember to make sure your Dockerfile is built with the appropriate base image or the required dependencies are installed.

init

The purpose of the init hook is to do any preparation needed for the run - this could be downloading + compiling code, downloading a pre-built artifact, or downloading external resources (pre-trained models, knowledge graphs, etc.).

The script will be executed as ./init --json <json> where the JSON string has the following format:

{
  "opts": { // extra options passed to the init script
      "<key>": "<value>"
   }
}

index

The purpose of the index hook is to build the indexes required for the run.

Before the hook is run, we will mount the document collections at a path passed to the script.

The script will be executed as: ./index --json <json> where the JSON string has the following format:

{
  "collections": [
    {
      "name": "<name>",              // the collection name
      "path": "/path/to/collection", // the collection path
      "format": "<format>"           // the collection format (trectext, trecweb, json, warc)
    },
    ...
  ],
  "opts": { // extra options passed to the index script
    "<key>": "<value>"
  },
}

train

The purpose of the train hook is to train a retrieval model.

The script will be executed as: ./train --json <json> where the JSON string has the following format:

{
  "topic": {
    "path": "/path/to/topic", // the path to the topic file
    "format": "trec"          // the format of the topic file
  },
  "qrels": {
    "path": "/path/to/qrel",  // the path to the qrel file
  },
  "model_folder": {
    "path": "/output",  // the path (in the docker image) where the output model folder (passed to the jig) is mounted
  },
  "opts": { // extra options passed to the train script
    "<key>": "<value>"
  },
}

search

The purpose of the search hook is to perform an ad-hoc retrieval run - multiple runs can be performed by calling jig multiple times with different --opts parameters.

The run files are expected to be placed in the /output directory such that they can be evaluated externally by jig using trec_eval.

The script will be executed as ./search --json <json> where the JSON string has the following format:

{
  "collection": {
    "name": "<name>"          // the collection name
  },
  "opts": { // extra options passed to the search script
    "<key>": "<value>"
  },
  "topic": {
    "path": "/path/to/topic", // the path to the topic file
    "format": "trec"          // the format of the topic file
  },
  "top_k": <int>              // the num of retrieval results for top-k retrieval
}

Note: If you're using the --timings option for the search hook, ensure that the time package (or bash) is installed in your Dockerfile.

interact

The purpose of the interact hook is to prepare for user interaction, assuming that any process started by init or index is gone.

The script will be executed as ./interact --json <json> where the JSON string has the following format:

{
  "opts": { // extra options passed to the interact script
    "<key>": "<value>"
  },
}

Note: If you need a port accessible, ensure you EXPOSE the port in your Dockerfile.

Azure Script

Run the script as follows: ./azure.sh --disk-name <disk_name> --resource-group <group> --vm-name <vm_name> --vm-size <vm_size> --run-file <file.json> --ssh-pubkey-path <path> --subscription <id>

The runs are defined in a JSON file, see azure.json as an example. Values in [] (i.e., [COLLECTION_PATH]) are replaced with the appropriate values defined in the file.

Notes

Python 3.5 or higher is required to run jig. Nvidia-docker is required to run images with gpu support, see https://github.com/NVIDIA/nvidia-docker for more details.